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      "source": [
        "本notebook文件来源：https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yJtIQiJbhgI"
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yh-mStQGbhgO"
      },
      "source": [
        "\n",
        "# Training a Classifier\n",
        "\n",
        "This is it. You have seen how to define neural networks, compute loss and make\n",
        "updates to the weights of the network.\n",
        "\n",
        "Now you might be thinking,\n",
        "\n",
        "## What about data?\n",
        "\n",
        "Generally, when you have to deal with image, text, audio or video data,\n",
        "you can use standard python packages that load data into a numpy array.\n",
        "Then you can convert this array into a ``torch.*Tensor``.\n",
        "\n",
        "-  For images, packages such as Pillow, OpenCV are useful\n",
        "-  For audio, packages such as scipy and librosa\n",
        "-  For text, either raw Python or Cython based loading, or NLTK and\n",
        "   SpaCy are useful\n",
        "\n",
        "Specifically for vision, we have created a package called\n",
        "``torchvision``, that has data loaders for common datasets such as\n",
        "Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz.,\n",
        "``torchvision.datasets`` and ``torch.utils.data.DataLoader``.\n",
        "\n",
        "viz.即\n",
        "\n",
        "This provides a huge convenience and avoids writing boilerplate code.\n",
        "\n",
        "boilerplate(可供模仿的)样板文件，文件范例\n",
        "\n",
        "For this tutorial, we will use the CIFAR10 dataset.\n",
        "It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,\n",
        "‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of\n",
        "size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.\n",
        "\n",
        "![figure](https://pytorch.org/tutorials/_images/cifar10.png)\n",
        "\n",
        "   cifar10\n",
        "\n",
        "\n",
        "Training an image classifier\n",
        "----------------------------\n",
        "\n",
        "We will do the following steps in order:\n",
        "\n",
        "1. Load and normalize the CIFAR10 training and test datasets using\n",
        "   ``torchvision``\n",
        "2. Define a Convolutional Neural Network\n",
        "3. Define a loss function\n",
        "4. Train the network on the training data\n",
        "5. Test the network on the test data\n",
        "\n",
        "\n",
        "### 1. Load and normalize CIFAR10\n",
        "\n",
        "Using ``torchvision``, it’s extremely easy to load CIFAR10.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuchFRQQbhgP"
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1NIVBgPbhgQ"
      },
      "source": [
        "The output of torchvision datasets are PILImage images of range [0, 1].\n",
        "We transform them to Tensors of normalized range [-1, 1].\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kpre5QCXbhgQ"
      },
      "source": [
        "<div class=\"alert alert-info\"><h4>Note</h4><p>If running on Windows and you get a BrokenPipeError, try setting\n",
        "    the num_worker of torch.utils.data.DataLoader() to 0.</p></div>\n",
        "\n"
      ]
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      "source": [
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "#transforms.Compose()\n",
        "#https://pytorch.org/vision/stable/transforms.html?highlight=transforms%20compose#torchvision.transforms.Compose\n",
        "#Composes several transforms together. \n",
        "\n",
        "#transforms.ToTensor()\n",
        "#https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.ToTensor\n",
        "#Convert a PIL Image or numpy.ndarray to tensor.\n",
        "\n",
        "#transforms.Normalize()\n",
        "\"\"\"\n",
        "Normalize a tensor image with mean and standard deviation. \n",
        "This transform does not support PIL Image. \n",
        "Given mean: (mean[1],...,mean[n]) and std: (std[1],..,std[n]) for n channels, \n",
        "this transform will normalize each channel of the input torch.*Tensor \n",
        "i.e., output[channel] = (input[channel] - mean[channel]) / std[channel]\n",
        "\"\"\"\n",
        "\n",
        "batch_size = 4\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,  download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,  shuffle=True, num_workers=2)\n",
        "#https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,  download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,  shuffle=False, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ],
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            "\n",
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Q_zfIuJbhgR"
      },
      "source": [
        "Let us show some of the training images, for fun.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8giva7H3bhgR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "a3cd3b45-bbe4-4192-b8ff-817464e0d214"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# functions to show an image\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# get some random training images\n",
        "dataiter = iter(trainloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(batch_size)))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            " deer  bird  ship truck\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C9yoEnHabhgR"
      },
      "source": [
        "### 2. Define a Convolutional Neural Network\n",
        "Copy the neural network from the Neural Networks section before and modify it to\n",
        "take 3-channel images (instead of 1-channel images as it was defined).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlZ5m__6bhgS"
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.view(-1, 16 * 5 * 5)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "net = Net()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1dHOlyObhgS"
      },
      "source": [
        "### 3. Define a Loss function and optimizer\n",
        "Let's use a Classification Cross-Entropy loss and SGD with momentum.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lx4b1aVobhgS"
      },
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrB4yIyQbhgT"
      },
      "source": [
        "### 4. Train the network\n",
        "\n",
        "This is when things start to get interesting.\n",
        "We simply have to loop over our data iterator, and feed the inputs to the\n",
        "network and optimize.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Xer7WH6bhgT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4692e062-2ee0-47b7-d17f-327f08063c70"
      },
      "source": [
        "for epoch in range(2):  # loop over the dataset multiple times\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "        # get the inputs; data is a list of [inputs, labels]\n",
        "        inputs, labels = data\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # print statistics\n",
        "        running_loss += loss.item()  \n",
        "        #https://pytorch.org/docs/stable/tensors.html?highlight=item#torch.Tensor.item\n",
        "        #Returns the value of this tensor as a standard Python number.\n",
        "        if i % 2000 == 1999:    # print every 2000 mini-batches\n",
        "            print('[%d, %5d] loss: %.3f' %\n",
        "                  (epoch + 1, i + 1, running_loss / 2000))\n",
        "            running_loss = 0.0\n",
        "\n",
        "print('Finished Training')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1,  2000] loss: 2.202\n",
            "[1,  4000] loss: 1.901\n",
            "[1,  6000] loss: 1.684\n",
            "[1,  8000] loss: 1.570\n",
            "[1, 10000] loss: 1.493\n",
            "[1, 12000] loss: 1.470\n",
            "[2,  2000] loss: 1.395\n",
            "[2,  4000] loss: 1.349\n",
            "[2,  6000] loss: 1.346\n",
            "[2,  8000] loss: 1.304\n",
            "[2, 10000] loss: 1.290\n",
            "[2, 12000] loss: 1.271\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U2x5Eoe4bhgT"
      },
      "source": [
        "Let's quickly save our trained model:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCkz9o6TbhgT"
      },
      "source": [
        "PATH = './cifar_net.pth'\n",
        "torch.save(net.state_dict(), PATH)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EeF7nOadbhgU"
      },
      "source": [
        "See [here](https://pytorch.org/docs/stable/notes/serialization.html)\n",
        "for more details on saving PyTorch models.\n",
        "\n",
        "### 5. Test the network on the test data\n",
        "\n",
        "We have trained the network for 2 passes over the training dataset.\n",
        "But we need to check if the network has learnt anything at all.\n",
        "\n",
        "We will check this by predicting the class label that the neural network\n",
        "outputs, and checking it against the ground-truth. If the prediction is\n",
        "correct, we add the sample to the list of correct predictions.\n",
        "\n",
        "Okay, first step. Let us display an image from the test set to get familiar.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sB0pUnDJbhgU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "a3574e7e-0c88-46bf-935c-74c271c26d64"
      },
      "source": [
        "dataiter = iter(testloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# print images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "GroundTruth:    cat  ship  ship plane\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-75GBcTbhgU"
      },
      "source": [
        "Next, let's load back in our saved model (note: saving and re-loading the model\n",
        "wasn't necessary here, we only did it to illustrate how to do so):\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atHILFq1bhgU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04f58042-73fa-4293-8212-35a1b76cac60"
      },
      "source": [
        "net = Net()\n",
        "net.load_state_dict(torch.load(PATH))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bpp1Se0XbhgU"
      },
      "source": [
        "Okay, now let us see what the neural network thinks these examples above are:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GzKGWqcjbhgV"
      },
      "source": [
        "outputs = net(images)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQqQ2z8UbhgV"
      },
      "source": [
        "The outputs are energies for the 10 classes.\n",
        "The higher the energy for a class, the more the network\n",
        "thinks that the image is of the particular class.\n",
        "So, let's get the index of the highest energy:\n",
        "\n",
        "这里的energy感觉可以理解为概率，属于某一类的概率"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xfwz6maJbhgV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "074e0244-affd-4406-ce5b-7300e087143f"
      },
      "source": [
        "_, predicted = torch.max(outputs, 1)\n",
        "\n",
        "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
        "                              for j in range(4)))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted:    cat  ship  ship  ship\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNAN40lwbhgV"
      },
      "source": [
        "The results seem pretty good.\n",
        "\n",
        "Let us look at how the network performs on the whole dataset.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FXVnpMfbhgV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae2b11c4-adac-421a-97e9-e88d29a085d0"
      },
      "source": [
        "correct = 0\n",
        "total = 0\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
        "    100 * correct / total))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 10000 test images: 54 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OtteqY5sbhgV"
      },
      "source": [
        "That looks way better than chance, which is 10% accuracy (randomly picking\n",
        "a class out of 10 classes).\n",
        "Seems like the network learnt something.\n",
        "\n",
        "Hmmm, what are the classes that performed well, and the classes that did\n",
        "not perform well:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWKcIFLYbhgW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ead779b4-8a3b-4a31-abc6-97766f32ed14"
      },
      "source": [
        "class_correct = list(0. for i in range(10))\n",
        "class_total = list(0. for i in range(10))\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        c = (predicted == labels).squeeze()\n",
        "        for i in range(4):\n",
        "            label = labels[i]\n",
        "            class_correct[label] += c[i].item()\n",
        "            class_total[label] += 1\n",
        "\n",
        "\n",
        "for i in range(10):\n",
        "    print('Accuracy of %5s : %2d %%' % (\n",
        "        classes[i], 100 * class_correct[i] / class_total[i]))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of plane : 53 %\n",
            "Accuracy of   car : 58 %\n",
            "Accuracy of  bird : 46 %\n",
            "Accuracy of   cat : 23 %\n",
            "Accuracy of  deer : 30 %\n",
            "Accuracy of   dog : 59 %\n",
            "Accuracy of  frog : 79 %\n",
            "Accuracy of horse : 59 %\n",
            "Accuracy of  ship : 71 %\n",
            "Accuracy of truck : 64 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mufZpFLbhgW"
      },
      "source": [
        "Okay, so what next?\n",
        "\n",
        "How do we run these neural networks on the GPU?\n",
        "\n",
        "Training on GPU\n",
        "----------------\n",
        "Just like how you transfer a Tensor onto the GPU, you transfer the neural\n",
        "net onto the GPU.\n",
        "\n",
        "Let's first define our device as the first visible cuda device if we have\n",
        "CUDA available:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hpQOh0LbbhgW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77fc8bcb-b6ca-4263-c15f-d7b07131bca1"
      },
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
        "\n",
        "print(device)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reKCVdTebhgW"
      },
      "source": [
        "The rest of this section assumes that ``device`` is a CUDA device.\n",
        "\n",
        "Then these methods will recursively go over all modules and convert their\n",
        "parameters and buffers to CUDA tensors:\n",
        "\n",
        "```python\n",
        "net.to(device)\n",
        "```\n",
        "\n",
        "Remember that you will have to send the inputs and targets at every step\n",
        "to the GPU too:\n",
        "\n",
        "```python\n",
        "inputs, labels = data[0].to(device), data[1].to(device)\n",
        "```\n",
        "\n",
        "Why don't I notice MASSIVE speedup compared to CPU? Because your network\n",
        "is really small.\n",
        "\n",
        "**Exercise:** Try increasing the width of your network (argument 2 of\n",
        "the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` –\n",
        "they need to be the same number), see what kind of speedup you get.\n",
        "\n",
        "**Goals achieved**:\n",
        "\n",
        "- Understanding PyTorch's Tensor library and neural networks at a high level.\n",
        "- Train a small neural network to classify images\n",
        "\n",
        "Training on multiple GPUs\n",
        "-------------------------\n",
        "If you want to see even more MASSIVE speedup using all of your GPUs,\n",
        "please check out [data_parallel_tutorial](https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html).\n",
        "\n",
        "Where do I go next?\n",
        "-------------------\n",
        "\n",
        "-  [Train neural nets to play video games](https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html)\n",
        "-  [Train a state-of-the-art ResNet network on imagenet](https://github.com/pytorch/examples/tree/master/imagenet)\n",
        "-  [Train a face generator using Generative Adversarial Networks](https://github.com/pytorch/examples/tree/master/dcgan)\n",
        "-  [Train a word-level language model using Recurrent LSTM networks](https://github.com/pytorch/examples/tree/master/word_language_model)\n",
        "-  [More examples](https://github.com/pytorch/examples)\n",
        "-  [More tutorials](https://github.com/pytorch/tutorials)\n",
        "-  [Discuss PyTorch on the Forums](https://discuss.pytorch.org/)\n",
        "-  [Chat with other users on Slack](https://pytorch.slack.com/messages/beginner/)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6Dgv-eabhgX"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}